


from sklearn.metrics import recall_score

import datasets


a_ :int  							=			'\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives and FN is the false negatives.\n'


a_ :Union[str, Any]  							=			'\nArgs:\n- **predictions** (`list` of `int`): The predicted labels.\n- **references** (`list` of `int`): The ground truth labels.\n- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.\n- **pos_label** (`int`): The class label to use as the \'positive class\' when calculating the recall. Defaults to `1`.\n- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n    - `\'binary\'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.\n    - `\'micro\'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.\n    - `\'macro\'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n    - `\'weighted\'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.\n    - `\'samples\'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.\n- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .\n    - `\'warn\'`: If there is a zero division, the return value is `0`, but warnings are also raised.\n    - `0`: If there is a zero division, the return value is `0`.\n    - `1`: If there is a zero division, the return value is `1`.\n\nReturns:\n- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.\n\nExamples:\n\n    Example 1-A simple example with some errors\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])\n        >>> print(results)\n        {\'recall\': 0.6666666666666666}\n\n    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)\n        >>> print(results)\n        {\'recall\': 0.5}\n\n    Example 3-The same example as Example 1, but with `sample_weight` included.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]\n        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)\n        >>> print(results)\n        {\'recall\': 0.55}\n\n    Example 4-A multiclass example, using different averages.\n        >>> recall_metric = datasets.load_metric(\'recall\')\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'macro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'micro\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=\'weighted\')\n        >>> print(results)\n        {\'recall\': 0.3333333333333333}\n        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'recall\': array([1., 0., 0.])}\n'


a_ :Optional[Any]  							=			'\n@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011}\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,       _KWARGS_DESCRIPTION			)
class 							lowercase  (						datasets.Metric			):







     def 							lowercase__       (						self :						int      ):
           return datasets.MetricInfo(
               description=_DESCRIPTION   ,   citation=_CITATION   ,   inputs_description=_KWARGS_DESCRIPTION   ,   features=datasets.Features(
                   {
                       '''predictions''': datasets.Sequence(datasets.Value('''int32'''      )      ),
                       '''references''': datasets.Sequence(datasets.Value('''int32'''      )      ),
                   }
                   if self.config_name == '''multilabel'''
                   else {
                       '''predictions''': datasets.Value('''int32'''      ),
                       '''references''': datasets.Value('''int32'''      ),
                   }      )   ,   reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html''']   ,   )







     def 							lowercase__       (						self :						Tuple   ,   _lowercase :						Optional[Any]   ,   _lowercase :						Optional[Any]   ,   _lowercase :						Optional[int]=None   ,   _lowercase :						Tuple=1   ,   _lowercase :						List[Any]="binary"   ,   _lowercase :						Any=None   ,   _lowercase :						Optional[int]="warn"   ,   ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		recall_score(
               _lowercase   ,   _lowercase   ,   labels=_lowercase   ,   pos_label=_lowercase   ,   average=_lowercase   ,   sample_weight=_lowercase   ,   zero_division=_lowercase   ,   )
           return {"recall": float(_lowercase      ) if score.size == 1 else score}










import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE     :					List[str]      =       get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE     :					Any      =       25_00_04
_SCREAMING_SNAKE_CASE     :					Any      =       25_00_20

@require_sentencepiece
@require_tokenizers
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						MBartaaTokenizer
    __magic_name__         =						MBartaaTokenizerFast
    __magic_name__         =						True
    __magic_name__         =						True
    def 						a_     (			self       ):
         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )
         tokenizer.save_pretrained(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							'''<s>'''
         snake_case											=							0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case       )     ,   __snake_case       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0]     ,   '''<s>'''       )
         self.assertEqual(vocab_keys[1]     ,   '''<pad>'''       )
         self.assertEqual(vocab_keys[-1]     ,   '''<mask>'''       )
         self.assertEqual(len(__snake_case       )     ,   1_0_5_4       )
    def 						a_     (			self       ):
         self.assertEqual(self.get_tokenizer().vocab_size     ,   1_0_5_4       )
    def 						a_     (			self       ):
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )

         snake_case											=							tokenizer.tokenize('''This is a test'''       )
         self.assertListEqual(__snake_case     ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__snake_case       )     ,   [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,   )

         snake_case											=							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']     ,   )
         snake_case											=							tokenizer.convert_tokens_to_ids(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             ]     ,   )

         snake_case											=							tokenizer.convert_ids_to_tokens(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']     ,   )
    @slow
    def 						a_     (			self       ):
         # fmt: off
         snake_case											=							{'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__snake_case     ,   model_name='''facebook/mbart-large-50'''     ,   revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''     ,   )




    def 						a_     (			self       ):
         if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

         snake_case											=							(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   snake_case											=							self.rust_tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )
                   snake_case											=							self.tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )

                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )
                   snake_case											=							tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f       )
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )
                        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=True
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=False
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )






@require_torch
@require_sentencepiece
@require_tokenizers
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						'facebook/mbart-large-50-one-to-many-mmt'
    __magic_name__         =						[
        ' UN Chief Says There Is No Military Solution in Syria',
        ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
    ]
    __magic_name__         =						[
        'Şeful ONU declară că nu există o soluţie militară în Siria',
        'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
        ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
        ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
    ]
    __magic_name__         =						[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]
    @classmethod
    def 						a_     (			cls       ):
         snake_case											=							MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''       )
         snake_case											=							1
         return cls
    def 						a_     (			self       ):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']     ,   2_5_0_0_0_1       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']     ,   2_5_0_0_0_4       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']     ,   2_5_0_0_2_0       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']     ,   2_5_0_0_3_8       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertIn(__snake_case     ,   self.tokenizer.all_special_ids       )
         snake_case											=							[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
         snake_case											=							self.tokenizer.decode(__snake_case     ,   skip_special_tokens=__snake_case       )
         snake_case											=							self.tokenizer.decode(generated_ids[1:]     ,   skip_special_tokens=__snake_case       )
         self.assertEqual(__snake_case     ,   __snake_case       )
         self.assertNotIn(self.tokenizer.eos_token     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							['''this is gunna be a long sentence ''' * 2_0]
         assert isinstance(src_text[0]     ,   __snake_case       )
         snake_case											=							1_0
         snake_case											=							self.tokenizer(__snake_case     ,   max_length=__snake_case     ,   truncation=__snake_case       ).input_ids[0]
         self.assertEqual(ids[0]     ,   __snake_case       )
         self.assertEqual(ids[-1]     ,   2       )
         self.assertEqual(len(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']       )     ,   [2_5_0_0_5_3, 2_5_0_0_0_1]       )
    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()
         snake_case											=							self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__snake_case       )
         snake_case											=							MBartaaTokenizer.from_pretrained(__snake_case       )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,   __snake_case       )
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   return_tensors='''pt'''       )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(
             self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=len(self.expected_src_tokens       )     ,   return_tensors='''pt'''     ,   )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         self.assertIsInstance(__snake_case     ,   __snake_case       )

         self.assertEqual((2, 1_4)     ,   batch.input_ids.shape       )
         self.assertEqual((2, 1_4)     ,   batch.attention_mask.shape       )
         snake_case											=							batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
         self.assertEqual(2     ,   batch.decoder_input_ids[0, 0]       )  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens     ,   [EN_CODE]       )
         self.assertEqual(self.tokenizer.suffix_tokens     ,   [self.tokenizer.eos_token_id]       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=3     ,   return_tensors='''pt'''       )
         snake_case											=							self.tokenizer(
             text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=1_0     ,   return_tensors='''pt'''       )
         snake_case											=							targets['''input_ids''']
         snake_case											=							shift_tokens_right(__snake_case     ,   self.tokenizer.pad_token_id       )

         self.assertEqual(batch.input_ids.shape[1]     ,   3       )
         self.assertEqual(batch.decoder_input_ids.shape[1]     ,   1_0       )




    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer._build_translation_inputs(
             '''A test'''     ,   return_tensors='''pt'''     ,   src_lang='''en_XX'''     ,   tgt_lang='''ar_AR'''       )

         self.assertEqual(
             nested_simplify(__snake_case       )     ,   {
                 # en_XX, A, test, EOS
                 '''input_ids''': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
                 '''attention_mask''': [[1, 1, 1, 1]],
                 # ar_AR
                 '''forced_bos_token_id''': 2_5_0_0_0_1,
             }     ,   )




